Evidence map›Paper›PMID 32592424›Full record

ArticleJournal of clinical pharmacology2020

Application of Physiologically Based Pharmacokinetic Modeling to Predict Drug Exposure and Support Dosing Recommendations for Potential Drug-Drug Interactions or in Special Populations: An Example Using Tofacitinib.

Susanna Tse, Martin E Dowty, Sujatha Menon, Pankaj Gupta, Sriram Krishnaswami

Abstract read
In one paragraph

Article in Journal of clinical pharmacology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  5. Article
  6. Review
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  9. Observational
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Susanna TseDepartment of Pharmacokinetics, Dynamics, and Metabolism, Pfizer Inc., Groton, Connecticut, USA.
Martin E DowtyDepartment of Pharmacokinetics, Dynamics, and Metabolism, Pfizer Inc., Cambridge, Massachusetts, USA.
Sujatha MenonDepartment of Clinical Pharmacology, Pfizer Inc., Groton, Connecticut, USA.
Pankaj GuptaWorldwide Business Development, Pfizer Inc., New York, New York, USA.
Sriram KrishnaswamiGlobal Product Development, Pfizer Inc., Groton, Connecticut, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tofacitinib is an oral Janus kinase inhibitor for the treatment of rheumatoid arthritis, psoriatic arthritis, and ulcerative colitis. It is eliminated via multiple pathways including oxidative metabolism (∼70%) and renal excretion (29%). This study aimed to predict the impact of drug-drug interactions and renal or hepatic impairment on tofacitinib pharmacokinetics using a physiologically based pharmacokinetic (PBPK) model. The model was developed using Simcyp based on the physicochemical properties and in vitro and in vivo pharmacokinetics data for tofacitinib. The model was verified by comparing the predicted pharmacokinetic profiles with those observed in available clinical studies after single or multiple doses of tofacitinib, as well as with tofacitinib as a victim of drug-drug interactions (because of inhibition of cytochrome P450 [CYP450] 3A4, CYP450 2C19, or CYP450 induction). In general, good agreement was observed between Simcyp predictions and clinical data. The results from this study provide confidence in using the PBPK modeling and simulation approach to predict the pharmacokinetics of tofacitinib under intrinsic (eg, renal or hepatic impairment) or extrinsic (eg, inhibition of CYP450 enzymes and/or renal transporters) conditions. This approach may also be useful in predicting pharmacokinetics under untested or complex situations (eg, when a combination of intrinsic and extrinsic factors may impact pharmacokinetics) when conducting clinical studies may be difficult, in response to health authority questions regarding dosing in special populations, or for labeling discussions.

Indexed as

Administration, OralAdultComputer SimulationCytochrome P-450 CYP2C19Cytochrome P-450 CYP3ACytochrome P-450 Enzyme InducersCytochrome P-450 Enzyme InhibitorsDrug Administration ScheduleDrug InteractionsFemaleHealthy VolunteersHumansInfusions, IntravenousLiver DiseasesMaleMiddle AgedCYP2C19 protein, humanCYP3A4 protein, humanCytochrome P-450 CYP2C19Cytochrome P-450 CYP3ACytochrome P-450 Enzyme InducersCytochrome P-450 Enzyme InhibitorsPiperidinesProtein Kinase InhibitorsPyrimidinestofacitinibactive renal secretiondrug-drug interactionshepatic impairmentmodelingPBPKrenal impairmentSimcyptofacitinib

Identifiers

PMID32592424
PMCPMC7689764

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.